Systems and methods herein generally relate to flow sequencing and more particularly, to systems and methods to identify and warn of out of sequencing of a moving item as multiple source lanes merge into destination lanes.
Computerized systems are useful for improving efficiencies in many areas, such as facilitating movement of items through controlled lanes or paths. Examples of such systems include movement of items on conveyor systems in warehouses; queuing of individuals at airports, amusement parks, and sporting events; processing drive through (or “drive-thru”) orders for food and other items at in-vehicle driving establishments; etc. The systems and methods herein will be described using the example of drive through ordering; however, these systems and methods are applicable to all types of processing where people or items merge from multiple source lanes into at least one destination lane.
In vehicle “drive through” operations customers can be served rapidly and cost effectively if certain functions are performed at multiple parallel stations (having respective lanes) and vehicles are merged to fewer lanes for stations that perform other functions. An example is a fast food restaurant having two or three parallel order stations, and a single lane for payment and food pick up. Inefficiencies and problems exist at the merge area following the parallel lanes. Currently, employees use video cameras and monitors to track the source lane of a vehicle as it crosses a merge line. This requires employee effort that could be used elsewhere and is subject to human error that wastes time and creates customer frustration at subsequent stations. This type of inefficiency can potentially translate into large amounts of lost revenue as backup in the drive-thru queue results in “drive-offs”, “drive-arounds” and “drive-bys” in which potential customers choose to seek food elsewhere for fear of waiting too long in line.
Especially during high volume times of day, the merging of vehicles into a single lane can be a random process. This leads to the sequence of orders in the system being different from the actual sequence of vehicles in the queue that is approaching the payment and pickup windows. For example, a first driver in an outer lane may place an order, then stay at the order station searching for their wallet to pay, while a second driver in an inner lane places their order after the first driver, but then merges before the first driver—thus the order sequence will be different from the vehicle sequence. Unfortunately, this can lead to inaccuracy—cars being asked to pay the wrong amount or being given the wrong food—as well as inefficient restaurant operations as employees must reshuffle the sequence of drinks and orders that have already been prepared. In particular, crew behavior such as entering an order after the vehicle has left the order point can affect overall system accuracy.